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A Locally Adaptive, Gradient-Free MCMC Method Inspired by the No-U-Turn Sampler
Markov Chain Monte Carlo (MCMC) methods are fundamental for sampling from complex probability distributions, but many widely used algorithms either rely on gradients (like NUTS) and/or struggle with high-dimensional, multi-scale … Read More
Why Go Coordinate-Free in Monte Carlo and Optimization?
Traditional methods like Gibbs sampling or randomized Kaczmarz rely heavily on specific coordinate systems, which can limit their efficiency—especially in ill-conditioned settings. But what happens when we step away from … Read More
Watch My Latest Talk on the No-U-Turn Sampler
Curious about the No-U-Turn Sampler and its performance in high-dimensional spaces? Watch my recent talk, where I present new insights into its reversibility and mixing time. There are still many … Read More